Conceptual Clustering

See: COBWEB, Formal Concept Analysis.

References

http://en.wikipedia.org/wiki/Conceptual_clustering
- Conceptual clustering is a machine learning paradigm for unsupervised classification developed mainly during the 1980s. It is distinguished from ordinary data clustering by generating a concept description for each generated class. Most conceptual clustering methods are capable of generating hierarchical category structures; see Categorization for more information on hierarchy. Conceptual clustering is closely related to formal concept analysis (FCA), decision tree learning, and mixture model learning.

(Fisher, 1987) ⇒ Douglas H. Fisher. (1987). “Knowledge Acquisition Via Incremental Conceptual Clustering.” In: Machine Learning Journal, 2(2). doi:10.1007/BF00114265
- Keywords: Conceptual clustering - concept formation - incremental learning - inference - hill climbing
- ABSTRACT: Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.